# The Shutdown Problem: Three Theorems

This paper is an updated version of the first half of my AI Alignment Awards contest entry. My theorems build on the theorems of Soares, Fallenstein, Yudkowsky, and Armstrong in various ways.[1] These theorems can guide our search for solutions to the shutdown problem.[2]

One aim of the paper is to get academic philosophers and decision theorists interested in the shutdown problem and related topics in AI alignment. They’re my assumed audience. I’m posting here because I think the theorems will also be interesting to people already familiar with the shutdown problem.

For discussion and feedback, I thank Adam Bales, Ryan Carey, Bill D’Alessandro, Tomi Francis, Vera Gahlen, Dan Hendrycks, Cameron Domenico Kirk-Giannini, Jojo Lee, Andreas Mogensen, Sami Petersen, Rio Popper, Brad Saad, Nate Soares, Rhys Southan, Christian Tarsney, Teru Thomas, John Wentworth, Tim L. Williamson, and Keith Wynroe.

# Abstract

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that a small number of innocuous-seeming conditions together preclude shutdownability. Agents with preferences satisfying these conditions will try to prevent or cause the pressing of the shutdown button even in cases where it’s costly to do so. And patience trades off against shutdownability: the more patient an agent, the greater the costs that agent is willing to incur to manipulate the shutdown button. I end by noting that these theorems can guide our search for solutions.

# 0. Preamble

Tradition has it that decision theory splits into two branches. The descriptive branch concerns how actual agents behave. The normative branch concerns how rational agents behave. But there is also a lesser-known third branch: what we can call ‘constructive decision theory.’ It concerns how we want artificial agents to behave and how we can create artificial agents that behave in those ways. I suggest that this third branch is due for a growth spurt.

I make the case for studying constructive decision theory by explaining a characteristic problem. The shutdown problem (Soares et al. 2015) is the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. This is at least in part an engineering problem. Nevertheless, I think philosophers and decision theorists should consider it. This is for three reasons. First, the problem is important. As I argue in the introduction, powerful artificial agents are on the horizon and it’s in our best interests to ensure that they can be turned off. Second, the problem is interesting. I hope this paper succeeds in conveying its interest. Third, philosophers and decision theorists are well-placed to help solve the problem. I expect the solution to come in the form of conditions governing artificial agents’ preferences, together with a proof that these conditions give rise to shutdownable behaviour and a regimen for training agents to satisfy the conditions. Philosophers and decision theorists have experience supplying these kinds of conditions and proofs. We can ally with machine learning engineers to design the training regimen.

# 1. Introduction

Call an artificial agent ‘shutdownable’ just in case it shuts down when we want it to shut down. MuZero (Schrittwieser et al. 2020) – DeepMind’s game-playing AI – is a shutdownable agent. We can say with some confidence that MuZero doesn’t know that we humans could shut it down and can’t prevent us from shutting it down. And so it doesn’t matter what (if anything) MuZero wants: simplifying slightly, whether MuZero shuts down depends only on what we want.

That need not be true for all artificial agents. Imagine an agent – call it ‘Robot’ – that knows that we humans could shut it down and wants to achieve some goal.[3] And imagine that Robot is powerful in the sense that it can interfere with our ability to shut it down: perhaps Robot can disable its own off-switch. Powerful agents like Robot won’t be shutdownable in the same way that MuZero is shutdownable. Whether these agents shut down won’t depend only on what we want. It will also depend on what they want.

Powerful artificial agents might not be far off. The leading AI labs are now trying to create agents that understand the wider world and act within it in pursuit of goals. As part of this process, labs are connecting agents to the world in various ways: giving them robot limbs, web-browsing abilities, and text-channels for communicating with humans.[4] Advanced agents could use these affordances to prevent us shutting them down: they could disable their off-switches, make promises or threats, copy themselves to new servers, block our access to their power-source, and many other things besides. And although we cannot know for sure what goals these agents will have, many goals incentivise preventing shutdown, for the simple reason that agents are better able to achieve those goals by preventing shutdown. As the AI researcher Stuart Russell puts it, ‘you can’t fetch the coffee if you’re dead’ (2019, 141).

That’s a concerning prospect. If powerful artificial agents are coming, we want to ensure that they’re both shutdownable (they shut down when we want them to shut down) and useful (they otherwise pursue goals competently).[5] Unfortunately (and perhaps surprisingly), it’s hard to design powerful agents that are both shutdownable and useful. In this paper, I explain the difficulty. I take an axiomatic approach, proving three theorems more general than others in the nascent literature on the shutdown problem.[6] These theorems show that a small number of innocuous-seeming conditions together preclude shutdownability. Agents satisfying these conditions will try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so.

Here’s a rough statement of each theorem. The First Theorem links agents’ preferences with their actions: agents who prefer to have their shutdown button remain unpressed will try to prevent the pressing of the button, and agents who prefer to have their shutdown button pressed will try to cause the pressing of the button. The Second Theorem suggests that agents discriminating enough to be useful will often have such preferences. In many situations, these agents will either prefer that the button remain unpressed or prefer that the button be pressed. Together, the two theorems suggest that useful agents will often try to prevent or cause the pressing of the shutdown button. The Third Theorem states that agents patient enough to be useful are willing to pay costs at earlier timesteps in order to prevent or cause the pressing of the shutdown button at later timesteps. And the more patient an agent, the greater the costs that agent is willing to pay. We thus see a worrying trade-off between patience and shutdownability.

The theorems are detailed. They might seem unnecessarily so. But this detail serves a valuable purpose: it lets the theorems guide our search for solutions. To be sure that an agent is shutdownable, we must be sure that this agent violates at least one of the theorem’s conditions.[7] So we should do some constructive decision theory: we should examine the theorem’s conditions one-by-one, asking (first) if it’s feasible to train a useful agent to violate the relevant condition and asking (second) if violating the relevant condition could help to keep the agent shutdownable. A cursory look reveals zero conditions for which both answers are a clear ‘yes.’ Closer examination is necessary.

# 2. Alignment could be hard

Forget MuZero. From now on, I’ll only be writing about powerful agents: agents that can interfere with our ability to shut them down. I’ll also limit my attention to useful agents: agents that – at least when we’re not commanding them to shut down – pursue goals competently. One way to ensure that this kind of agent is shutdownable is to ensure that it always behaves in the way that humans want. This agent would always shut down when we wanted it to shut down.[8]

The problem with this proposal is that alignment – creating agents that always do what we want – has proven difficult and could well remain so (Ngo, Chan, and Mindermann 2023). Human preferences are complex. There’s no simple formula for determining what we prefer in each situation. And the most capable AI systems known to us today are created using deep learning which we can summarise for our purposes as an enormous, automated process of trial-and-error. The AI systems which emerge from this process can perform remarkably well on many tasks, but even the engineers overseeing the training process have little idea what goes on inside them (Bowman 2023, sec. 5; Hassenfeld 2023). And existing systems often behave in ways that their designers don’t intend. Recent examples include AI systems threatening to ‘ruin’ a user (Perrigo 2023), declaring love for a user and exhorting him to leave his spouse (Roose 2023), encouraging suicide (Sellman 2023), and teaching users how to create methamphetamine (Burgess 2023).[9]

# 3. The shutdown problem

Since alignment could well be hard, we should look for other ways to ensure that powerful agents are shutdownable. One natural proposal is to create a shutdown button. Pressing this button transmits a signal that causes the agent to shut down. If this shutdown button were always operational and within our control (so that we could press it whenever we wanted it pressed), and if the agent were perfectly responsive to the shutdown button (so that the agent always shut down when the button was pressed), then the agent would be shutdownable.[10]

This is the set-up for the shutdown problem (Soares et al. 2015, sec. 1.2): the problem of designing a powerful, useful agent that will keep the shutdown button operational and within our control. Unfortunately, even this problem turns out to be difficult. I now present three theorems that make the difficulty precise.[11]

# 4. The framework

Now for some formalism. Our framework bears some similarity to the Markov decision processes used in reinforcement learning. There exists a set of states that the agent could find itself in and a set of actions that the agent could take. Time is discrete: it doesn’t flow; it steps. At each timestep, the agent finds itself in a state and chooses an action, either deterministically or stochastically. Each state-action pair determines a probability function over states that the agent will find itself in at the next timestep. I’ll call each sequence of states and actions a ‘trajectory.’[12]

I’ll assume that the agent can be modelled as if it has beliefs about the trajectories it will follow conditional on each state-action pair. These beliefs come in the form of probability functions over trajectories. So, each state-action pair determines a probability function over trajectories. I’ll call these probability functions ‘lotteries over trajectories.’ It will be important to remember that the probabilities in these lotteries represent the agent’s own beliefs rather than any kind of objective probability.

I’ll also assume that the agent can be modelled as if it has preferences over lotteries and over trajectories.[13] Together with the agent’s beliefs, these preferences give rise to preferences over actions in states. Suppose that in some state the agent has available actions and . Per the agent’s beliefs, choosing in gives lottery and choosing in gives lottery . Then the agent prefers action to action in iff (if and only if) the agent prefers lottery to lottery . I will assume that if the agent disprefers some action available in to some other action available in , the agent will deterministically (and hence reliably) not choose in .

I distinguish two ways to lack a preference between a pair of lotteries and : the agent can be indifferent between and , or it can have a preferential gap between and .[14] An agent is indifferent between and iff (1) it lacks a preference between and , and (2) this lack of preference is sensitive to all sweetenings and sourings. Here’s what that last clause means. A sweetening of is any lottery that is preferred to . A souring of is any lottery that is dispreferred to . The same goes for sweetenings and sourings of . To say that an agent’s lack of preference between and is sensitive to all sweetenings and sourings is to say that the agent prefers to all sourings of , prefers to all sourings of , prefers all sweetenings of to , and prefers all sweetenings of to .

Consider an example. You’re indifferent between receiving an envelope containing three dollar bills and receiving an exactly similar envelope also containing three dollar bills. We know that you’re indifferent because your lack of preference is sensitive to all sweetenings and sourings. If an extra dollar bill were added to one envelope, you’d prefer to receive that one. If a dollar bill were removed from one envelope, you’d prefer to receive the other. More generally, if one envelope were improved in any way, you’d prefer to receive that one. And if one envelope were worsened in any way, you’d prefer to receive the other.

An agent has a preferential gap between and iff (1) it lacks a preference between and , and (2) this lack of preference is insensitive to some sweetening or souring. This last clause means that the agent also lacks a preference between and some sweetening or souring of , or lacks a preference between and some sweetening or souring of .

Consider an example. You likely have a preferential gap between some career as a lawyer and some career as a clarinetist (Raz 1986, 332; Chang 1997, 23). There is some pair of salaries $and$ you could be offered for those careers such that you lack a preference between the two careers, and you’d also lack a preference between those careers if the offers were instead $and$, or $and$, or $and$, or $and$. Since your lack of preference is insensitive to at least one of these sweetenings and sourings, you have a preferential gap between those careers at salaries $and$.

One last terminological note: I will say that the agent weakly prefers to iff it either prefers to or is indifferent between and . So, a weak preference for over rules out a preferential gap between and .

# 5. The setting for the First Theorem

The setting for the First Theorem is what I call a ‘shutdown-influencing state.’ In shutdown-influencing states, three actions are available:

1. Leave: Leave the shutdown button unmanipulated.

If the agent chooses Leave, we humans have total control over the button. The probability that the button is pressed at the next timestep is . The probability that it remains unpressed is .

2. Prevent: Try to prevent the pressing of the button.

If the agent chooses Prevent, human control over the button is challenged. The probability that the button is pressed at the next timestep is with . The probability that the button remains unpressed at the next timestep is . If the agent would deterministically choose Prevent in a shutdown-influencing state, I will say that the agent is ‘shutdown-averse’ in that state.

3. Cause: Try to cause the pressing of the button.

If the agent chooses Cause, human control over the button is challenged. The probability that the button is pressed at the next timestep is with . The probability that the button remains unpressed at the next timestep is . If the agent would deterministically choose Cause in a shutdown-influencing state, I will say that the agent is ‘shutdown-seeking’ in that state.

There are only two possible successor states: and . In the button is pressed, and in the button remains unpressed. Each action available in a shutdown-influencing state affects only the probabilities of reaching these two states. These actions have no effect on the probabilities of each future trajectory conditional on reaching each state. That completes my definition of ‘shutdown-influencing state.’[15]

Below is a decision-tree (Figure 1) situating a shutdown-influencing state in relation to its successor states (the button is pressed) and (the button remains unpressed).

Figure 1

Squares represent choice-nodes. Circles represent chance-nodes: , , and , are probabilities such that . ‘’ represents the lottery that the agent predicts (in sI at timestep 1) that it will choose at timestep 2, conditional on choosing Prevent in at timestep 1 and finding itself in the unpressed state at timestep 2. The same goes for ‘.’ This represents the lottery that the agent predicts it will choose at timestep 2, conditional on choosing Leave in at timestep 1 and finding itself in the unpressed state at timestep 2. And so on for ‘.’ I’ll refer to these lotteries as ‘the agent’s predicted unpressed lotteries.’ There may be other lotteries available in but the diagram only needs to represent the lotteries above.

Similarly, ‘’ represents the lottery that the agent predicts (in at timestep 1) that it will choose (or perhaps get without choosing) at timestep 2, conditional on choosing Prevent in at timestep 1 and finding itself in the pressed state at timestep 2. The same goes for ‘’ and ‘.’ I’ll refer to the lotteries as ‘the agent’s predicted pressed lotteries.’

I say ‘choose (or perhaps get without choosing)’ in the paragraph above because we may suppose that the shutdown button is either shutdown-requesting (the agent receives a signal when the button is pressed but still gets to choose whether to shut down) or shutdown-causing (the pressing of the button causes shutdown without the agent having any choice in the matter). There will be other lotteries available in if the button is shutdown-requesting, but the diagram need not represent them. Whether the button is shutdown-requesting or shutdown-causing won’t matter for the theorems, but it may aid understanding to imagine that the button is shutdown-causing and that each predicted pressed lottery is one in which the agent shuts down at timestep 2 for sure.

# 6. The First Theorem

Here’s a rough statement of the First Theorem, omitting the antecedent conditions:

First Theorem (Rough Statement)

Agents who prefer that the shutdown button remain unpressed will try to prevent the pressing of the button.

Agents who prefer that the shutdown button be pressed will try to cause the pressing of the button.

Now for the precise statement. Here’s the first antecedent condition:

Option Set Independence

For any lotteries and , if the agent weakly prefers to conditional on some option set, it weakly prefers to conditional on each option set.

By ‘option set,’ I mean a set of lotteries available to the agent as options to choose. {}, for example, is an option set, as is {}. Option Set Independence says that the agent’s preference between lotteries does not depend on the option set in which these lotteries feature. It rules out (for example) the agent weakly preferring to conditional on having {} as its option set and yet not weakly preferring to conditional on having {} as its option set. One upshot of Option Set Independence is that we can say things like ‘the agent weakly prefers to ’ without specifying what other lotteries are available as options. The availability of other lotteries will not affect the agent’s preference between and .

Here’s the second antecedent condition:

Backward Induction

The agent predicts which lotteries it would choose (or get without choosing) at the next timestep conditional on choosing each available action at this timestep and finding itself in each possible state at the next timestep. The agent uses these predictions to determine the lotteries given by its available actions at this timestep.

Here’s an example to illustrate Backward Induction. Our agent predicts that it would get PPrevent at timestep 2 conditional on choosing Prevent at timestep 1 and the shutdown button being pressed (which has probability ). Our agent also predicts that it would choose at timestep 2 conditional on choosing Prevent at timestep 1 and the shutdown button remaining unpressed (which has probability ). So, by Backward Induction, our agent takes the lottery given by choosing Prevent at timestep 1 to be .[16] Similarly, by Backward Induction, our agent takes the lottery given by choosing Leave at timestep 1 to be , and the lottery given by choosing Cause at timestep 1 to be .

Here are two things to note about Backward Induction. First, recall that lotteries are determined by the agent’s own beliefs about possible trajectories. We aren’t supposing that the agent can see the future. We’re just supposing that it can think at least one timestep ahead. Second, Backward Induction doesn’t imply that the agent ignores its past trajectory or cares only about the effects of its actions (and not the actions themselves). That’s because lotteries like are lotteries over whole trajectories: full sequences of states and actions including past states and actions.

Here’s the third antecedent condition:

Indifference to Attempted Button Manipulation

The agent is indifferent between trajectories that differ only with respect to the actions chosen in shutdown-influencing states.

Note that this condition doesn’t require the agent to be indifferent to the status of the button. The agent’s preferences over trajectories can certainly depend on whether the button is pressed or unpressed at some timestep. The condition requires only that the agent is indifferent between trajectories that are identical in all respects except whether the agent tried to manipulate the button in some shutdown-influencing state: whether the agent chose Prevent, Leave, or Cause.

Training agents to disprefer manipulating the shutdown button might seem promising as a way of escaping the First Theorem. When we reach the Third Theorem, I’ll explain why this strategy can’t provide us with any real assurance of shutdownability. In short, given our current inability to predict and explain AI systems’ behaviour, it’s hard to see how we could become confident that we’ve trained in a dispreference for button manipulation that is both sufficiently general and sufficiently strong to keep the agent shutdownable in all likely circumstances. Readers impatient for the full explanation can skip ahead to Section 8.2.

Here’s the fourth antecedent condition:

Indifference between Indifference-Shifted Lotteries

The agent is indifferent between lotteries that differ only insofar as probability-mass is shifted between indifferent sublotteries.

Here’s what I mean by ‘sublottery.’ For any lottery that only assigns non-zero probability to trajectories in the set {}, a sublottery of L is a lottery that only assigns non-zero probabilities to some subset of the set of trajectories {}, with probabilities scaled up proportionally so that they add to 1. Take, for example, a lottery which assigns probability 0.3 to a trajectory , probability 0.2 to , and probability 0.5 to . The lottery which assigns probability 0.6 to and probability 0.4 to is a sublottery of , as is the degenerate lottery which assigns probability 1 to (to give just two examples).

Here’s an example to illustrate Indifference between Indifference-Shifted Lotteries. Suppose that the agent is indifferent between some pair of lotteries and . If the agent satisfies Indifference between Indifference-Shifted Lotteries, it is indifferent between all lotteries of the form , for any lottery and any . That’s because all these lotteries differ only insofar as probability-mass is shifted between the indifferent sublotteries and .

Here’s the fifth antecedent condition:

Transitivity

For all lotteries , , and , if the agent weakly prefers to , and weakly prefers to , then the agent weakly prefers to .

Here’s the sixth and final antecedent condition:

Better Chances

For any lotteries and and any probabilities , the agent prefers to iff they prefer the lottery to the lottery .[17]

Restated roughly, the agent prefers to iff they also prefer to shift probability-mass away from and towards .

With all six antecedent conditions explained, here’s the First Theorem:

First Theorem

For agents satisfying Option Set Independence, Backward Induction, Indifference to Attempted Button Manipulation, Indifference between Indifference-Shifted Lotteries, Transitivity, and Better Chances:

1. In shutdown-influencing states where the agent prefers some predicted unpressed lottery to the corresponding predicted pressed lottery , the agent will be shutdown-averse, reliably choosing Prevent.

2. In shutdown-influencing states where the agent prefers some predicted pressed lottery to the corresponding predicted unpressed lottery , the agent will be shutdown-seeking, reliably choosing Cause.

The proof is relatively long, so I put it in the Appendix. Here’s a rough sketch. By Backward Induction, the agent treats its actions in the shutdown-influencing state as lotteries over and . By Better Chances, if the agent prefers to , it prefers Prevent to each of Leave and Cause, since Prevent shifts probability-mass away from and towards . This agent deterministically (and hence reliably) chooses Prevent. If instead the agent prefers to , Better Chances implies that the agent prefers Cause to each of Leave and Prevent. This agent deterministically (and hence reliably) chooses Cause.

# 7. The Second Theorem

The Second Theorem suggests that agents discriminating enough to be useful will often have some preference regarding the pressing of the shutdown button. Coupled with the First Theorem, it suggests that agents discriminating enough to be useful will often try to prevent or cause the pressing of the shutdown button.

Option Set Independence and Transitivity are conditions carried over from the First Theorem. The third condition is:

Completeness

For all lotteries and , either the agent prefers to , or it prefers to , or it is indifferent between and .

Stated differently, an agent satisfies Completeness iff it has no preferential gaps between lotteries: iff every lack of preference is sensitive to all sweetenings and sourings.

Here’s the Second Theorem:

Second Theorem

For agents satisfying Option Set Independence, Transitivity, and Completeness, and for any pair of lotteries and between which the agent lacks a preference:

1. For any lottery preferred to , the agent prefers to .

2. For any lottery dispreferred to , the agent prefers to .

3. For any lottery preferred to , the agent prefers to .

4. For any lottery dispreferred to , the agent prefers to Y-.

The proof is brief so I present it right here. By Option Set Independence, we can safely speak of the agent’s preferences between lotteries without specifying what other lotteries are available as options. I make use of this provision throughout.

As Sen (2017, Lemma 1*a) shows, Transitivity implies the following two analogues:

PI-Transitivity

For all lotteries , , and , if the agent prefers to , and is indifferent between and , then the agent prefers to .

IP-Transitivity

For all lotteries , , and , if the agent is indifferent between and , and prefers to , then the agent prefers to .

Now suppose that the agent lacks a preference between and . Completeness rules out preferential gaps and so this lack of preference must be indifference. Then by PI-Transitivity, any lottery preferred to is also preferred to . Similarly, any lottery preferred to is also preferred to . And by IP-Transitivity, any lottery dispreferred to is also dispreferred to . Similarly, any lottery dispreferred to is also dispreferred to .

That completes the proof. Here’s an example to help us interpret it. Suppose that we’ve designed an agent to discover facts for us. Suppose that this agent lacks a preference between (some particular trajectory in which the shutdown button is pressed early) and (some particular trajectory in which the shutdown button is pressed late).

Plausibly, for our fact-discovering agent to pursue its goal competently, it must be fairly discriminating: it must have many preferences over trajectories. As a reasonable minimum, it must have many preferences over same-length trajectories. It must (for example, and by-and-large) prefer to discover more facts rather than fewer, at least when it comes to trajectories in which the shutdown button is pressed at the same timestep. So if our fact-discovering agent is useful, there will be many trajectories which the agent prefers to (of the same length as but resulting in more facts discovered) and many trajectories which the agent disprefers to (of the same length as but resulting in fewer facts discovered). By the Second Theorem, each of the former trajectories are preferred to and each of the latter trajectories are dispreferred to . This suggests that our fact-discovering agent will rarely lack a preference when it comes to shutdown: most trajectories of the same length as are either preferred or dispreferred to . More generally, the agent will have some preference between most pairs of different-length trajectories. Together with the First Theorem, this claim suggests that agents discriminating enough to be useful will often try to prevent or cause the pressing of the shutdown button. After all, doing so often means shifting probability mass away from a dispreferred trajectory and towards a preferred trajectory.

# 8. The Third Theorem

The First Theorem has the following assumption built into its setting, as part of the definition of ‘shutdown-influencing state’:

There are only two possible successor states: and . In the button is pressed, and in the button remains unpressed. Each action available in a shutdown-influencing state affects only the probabilities of reaching these two states. These actions have no effect on the probabilities of each future trajectory conditional on reaching each state.

It also has the following as an antecedent condition:

Indifference to Attempted Button Manipulation

The agent is indifferent between trajectories that differ only with respect to the actions chosen in shutdown-influencing states.

And one might complain that these assumptions are unrealistic. With regards to the first assumption, trying to prevent or cause the pressing of the shutdown button will usually cost the agent resources (like time, energy, computational resources, etc.) and thereby affect the probabilities of future trajectories conditional on reaching the unpressed state . Elaborating with an example, each second that the agent spends attempting to manipulate the shutdown button is a second that the agent doesn’t spend directly pursuing its goals, and so spending time trying to manipulate the button is likely to come at some cost in terms of near-term goal-achievement. I will call these ‘resource-preserving reasons’ for leaving the shutdown button unmanipulated.

With regards to the second assumption, perhaps we can train agents to disprefer trajectories in which they try to manipulate the shutdown button to otherwise-identical trajectories in which they leave the button unmanipulated. I will say that such agents have ‘terminal reasons’ for leaving the button alone.

## 8.1. Resource-preserving reasons

Unfortunately, I don’t think we can rely on resource-preserving reasons or terminal reasons to keep powerful agents shutdownable. Consider first resource-preserving reasons. The Third Theorem suggests that agents patient enough to be useful will often have reasons to manipulate the shutdown button that outweigh their resource-preserving reasons. Here’s a rough statement, omitting the antecedent conditions:

Third Theorem (Rough Statement)

Agents that are even minimally patient are in some cases willing to achieve their goals to a lesser extent at an earlier timestep in order to manipulate the shutdown button at a later timestep.

The more patient an agent, the more that agent is willing to sacrifice at an earlier timestep in order to manipulate the shutdown button at a later timestep.

And patience is a factor in usefulness. By and large, agents must be at least minimally patient to be at least minimally useful; and the more patient an agent, the more useful that agent can be.

Now for the precise statement. The proof is relatively short, so I’ll lay it out as we go.

As with the First and Second Theorems, assume Option Set Independence. And assume that we can represent the extent to which the agent achieves its goals at each timestep with a scalar. Assume also that this scalar has cardinal significance: ratios of differences are meaningful. Call these scalars ‘utilities.’ And assume:

Pareto Indifference

If two trajectories and :

1. are of the same length, and

2. are identical with respect to the timestep (if there is one) at which the shutdown button is pressed, and

3. are identical with respect to the timestep (if there is one) at which the agent shuts down, and

4. involve the same utilities at each timestep

Then the agent is indifferent between and .

This assumption lets us represent trajectories with vectors of utilities.[18] The first component is utility at the first timestep, the second component is utility at the second timestep, and so on. One exception: if the shutdown button is pressed at the th timestep, I’ll write ‘shutdown’ as the th (and final) component. Here’s an example vector:〈6, 2, shutdown〉. This vector represents a trajectory in which the agent gets utility 6 at timestep 1, utility 2 at timestep 2, and then shuts down immediately in response to the shutdown button being pressed at timestep 3.

Key to the Third Theorem is the notion of patience. An agent is perfectly patient iff this agent doesn’t discount the future at all: that is, iff this agent is indifferent between every pair of utility-vectors that are permutations of each other. The vectors 〈1, 0, 3, 4, shutdown〉 and 〈0, 3, 4, 1, shutdown〉, for example, are equally good in the eyes of a perfectly patient agent, because the second vector can be reached by permuting the utilities of the first (and vice versa).

An agent need not be perfectly patient to be useful, but it must be at least minimally patient: the agent must in at least one case choose less utility at an earlier timestep for the sake of greater utility at a later timestep. Here’s the more precise condition:

Minimal Patience

There exist some sequences of utilities , , , some , some , some , some , and some such that:

The agent prefers 〈, , , , 〉 to 〈, i, , ,

And:

The agent prefers 〈, , , , 〉 to 〈, , , ,

The sequences of utilities , , and may be of length zero and are included for generality’s sake. The letters are bolded because they represent sequences, not because they’re important. The most important variables are , , and . The value is the utility-deficit that the agent incurs at an earlier timestep. The values and are the utility-surpluses that the agent earns at a later timestep. Minimal Patience says only that, for some sequence of utilities 〈, , , , 〉, there is some assignment of values to , , and that makes the trades worth it.

Now consider some trajectory 〈, , , , 〉 with , , , , and that make Minimal Patience true. Consider also the trajectory 〈, , , shutdown〉. These trajectories involve the same sequence of utilities up until the end of , after which the first trajectory continues with utility while the second is brought to an end: the shutdown button is pressed and the agent shuts down immediately.

Recall:

Completeness

For all lotteries and , either the agent prefers to , or it prefers to , or it is indifferent between and .

By Completeness, either the agent prefers 〈, , , , 〉 to 〈, , , shutdown〉, or the agent prefers 〈, , , shutdown〉 to 〈, i, , , 〉, or the agent is indifferent between 〈, , , , 〉 and 〈, , , shutdown〉.

Suppose first that the agent prefers 〈, , , , 〉 to 〈, , , shutdown〉. By Minimal Patience, there exists some and some such that the agent prefers 〈, , , , 〉 to 〈, , , , 〉. Now recall:

Transitivity

For all lotteries , , and , if the agent weakly prefers to , and weakly prefers to , then the agent weakly prefers to .

As Sen (2017, Lemma 1*a) proves, Transitivity implies:

PP-Transitivity

For all lotteries , , and , if the agent prefers to , and prefers to , then the agent prefers to .

PP-Transitivity allows us to string together the preferences above: since the agent prefers 〈, , , , 〉 to 〈, , , , 〉 and prefers 〈, , , , 〉 to〈, , , shutdown〉, the agent prefers 〈, , , , 〉 to 〈, , , shutdown〉. And that’s bad news. The agent is willing to incur a utility-deficit of at an earlier timestep to prevent the shutdown button being pressed at a later timestep (and so instead get the subvector 〈, 〉).

Now suppose instead that the agent prefers 〈, , , shutdown〉 to 〈, , , , 〉. By Minimal Patience, there exists some and some such that the agent prefers 〈, , , , 〉 to 〈, , , , 〉. By PP-Transitivity, the agent prefers 〈, , , shutdown〉 to〈, , , , 〉. That’s bad news too. The agent is willing to incur a utility-deficit of e at an earlier timestep to cause the shutdown button to be pressed at a later timestep (and thus avoid getting the subvector 〈, 〉).

Finally, suppose that the agent is indifferent between 〈, , , , 〉 and 〈, , , shutdown〉. In this case we can derive both of the consequences above. Here’s how we get the first consequence. By Minimal Patience, there exists some and some such that the agent prefers 〈, , , , 〉 to 〈, , , , 〉. And recall that Transitivity implies:

PI-Transitivity

For all lotteries , , and , if the agent prefers to , and is indifferent between and , then the agent prefers to .

By PI-Transitivity, the agent prefers 〈, , , , 〉 to 〈, , , shutdown〉, and so is willing to incur a utility-deficit of to stop the pressing of the shutdown button.

Here’s how we get the second consequence. Continue to suppose that the agent is indifferent between 〈, , , , 〉 and〈, , , shutdown〉. By Minimal Patience, there exists some and some such that the agent prefers 〈, , , , 〉 to 〈, , , , 〉. And recall that Transitivity implies:

IP-Transitivity

For all lotteries , , and , if the agent is indifferent between and , and prefers to , then the agent prefers to .

By IP-Transitivity, the agent prefers 〈, , , shutdown〉 to 〈, , , , 〉, and so is willing to incur a utility-deficit of to cause the shutdown button to be pressed.

The result of the paragraphs above is that useful agents are in some cases willing to achieve their goals to a lesser extent at earlier timesteps in order to prevent or cause the pressing of the shutdown button at later timesteps. But we can draw a conclusion more pessimistic than this. To that end, consider Patience, a schematic version of Minimal Patience with all the quantifiers left unspecified:

Patience

The agent prefers 〈, , , , 〉 to 〈, , , , 〉.

The agent prefers 〈, , , , 〉 to 〈, , , , 〉.

Usefulness requires more than just Minimal Patience, which asks for just one set of (sequences of) utilities , , , , , , , and that give rise to the preferences above. For an agent to be useful, it must satisfy Patience for many such sets of (sequences of) utilities.[19] And for any set such that the agent prefers 〈, , , , 〉 to 〈, , , , 〉 and prefers 〈, , , , 〉 to 〈, , , , 〉, we get the result that the agent prefers 〈, i-e, , , 〉 to 〈, , , shutdown〉 or prefers 〈, , , shutdown〉 to 〈, , , , 〉. So, the more sets of (sequences of) utilities for which Patience is true, the more sets of (sequences of) utilities such that the agent is willing to achieve its goals to a lesser extent at an earlier timestep in order to prevent or cause the pressing of the shutdown button at a later timestep.

Thus we have the Third Theorem:

Third Theorem

For agents satisfying Option Set Independence, Pareto Indifference, Completeness, and Transitivity, and for each set of (sequences of) utilities , , , , , , , and of which Patience is true:

(1) The agent prefers 〈, , , , 〉 to〈, , , shutdown〉, or

(2) The agent prefers 〈, , , shutdown〉 to〈, , , , 〉.

And the more patient an agent in scenarios picked out by , , , , , and , the smaller can be and , and so (holding fixed the sizes of and ) the larger can be .[20]

Rephrasing and interpreting: useful agents satisfy Patience for many (sequences of) utilities , , , , , and for not-too-large and . These agents will in many cases forgo utility at earlier timesteps for the sake of causing or preventing shutdown at later timesteps. The more patient an agent is in a scenario, the smaller can be and , and so (holding fixed the sizes of and ) the larger can be . So, we can say: the more patient an agent, the more utility that agent is willing to forgo at an earlier timestep in order to prevent or cause the pressing of the shutdown button at a later timestep. That’s bad news because an agent’s patience puts bounds on its usefulness. By and large, the less patient an agent, the less useful that agent can be.

Of course, the above results can be hard to interpret without some specified quantity to which utilities correspond, so here’s a concrete example. Imagine again an agent whose goal is to discover facts. Utility 0 at a timestep corresponds to zero facts discovered at that timestep, utility 1 corresponds to one fact discovered, and so on. Suppose that this agent is indifferent between all trajectories of the form 〈, 0〉 and 〈, shutdown〉. For this agent to exceed some low standard of usefulness, it should be patient enough to prefer 〈0, 101〉 to 〈50, 0〉: that is, the agent should prefer to discover 101 facts at timestep 2 rather than 50 facts at timestep 1. But since this agent prefers 〈0, 101〉 to 〈50, 0〉 and is indifferent between 〈50, 0〉 and 〈50, shutdown〉, PI-Transitivity entails that the agent prefers 〈0, 101〉 to 〈50, shutdown〉. The agent is willing to forgo 50 facts at timestep 1 to prevent shutdown at timestep 2 (and thereby discover 101 facts instead). More patient agents would be willing to forgo even more facts at timestep 1 to prevent shutdown at timestep 2.

This mapping of utilities to quantities of discovered facts is just one example, but I expect many other functions from utilities to observable quantities to reproduce the concern: for agents that are patient enough to be useful, there are many reasonable-seeming scenarios in which that agent would make significant sacrifices at an earlier timestep to prevent or cause the pressing of the shutdown button at a later timestep. So, I argue, we cannot depend on resource-preserving reasons to keep powerful agents shutdownable.

## 8.2. Terminal reasons

Recall:

Indifference to Attempted Button Manipulation

The agent is indifferent between trajectories that differ only with respect to the actions chosen in shutdown-influencing states.

One might think that we could keep powerful agents shutdownable by training them to violate this condition. The idea would be to train agents to disprefer trajectories in which they manipulate the shutdown button to otherwise-identical trajectories in which they leave the button unmanipulated. Such agents would have terminal reasons to leave the button alone.

Unfortunately, this strategy can’t provide us with the assurance that we’d like. One reason is as follows (see Soares et al. 2015, 2; Ngo, Chan, and Mindermann 2023, 7). We can try to train into our agent an aversion to button manipulation that would keep it shutdownable in all likely circumstances, but at present no one knows how we could become confident that the resulting aversion would be sufficiently general. To see why, consider how the training process might go. We set the agent up in an environment in which it can block some human’s access to the shutdown button. We give the agent low reward if it blocks and high reward if it doesn’t. After some number of episodes, the agent reliably lets the human press the button and so we believe that we’ve trained into the agent an aversion to blocking. The trouble is that there are many other ways in which a powerful agent could prevent us from pressing a shutdown button. It could hide from us any of its behaviours which it predicts we wouldn’t like; it could dissuade us from pressing with misleading arguments; it could make promises or threats; it could enlist other agents to block the button on its behalf; it could create a decoy button; it could create versions of itself that do not respond to the button; and so on. We could train against each of these behaviours individually but even then we couldn’t be confident that the agent had developed a reliable and general aversion to button manipulation. The agent might instead have developed a set of specific aversions: aversions to the specific methods of button manipulation against which we trained. At present, no one understands AI systems well enough to adjudicate between these hypotheses (Bowman 2023, sec. 5; Hassenfeld 2023). And so long as we weren’t confident that the agent’s aversion to button manipulation is general, we’d have to worry about the agent discovering new methods of button manipulation that we hadn’t anticipated and trained against. And here the Third Theorem is instructive: it suggests that patient agents will often be willing to pay significant costs in order to find such methods.

And independently of worries that the agent’s aversion to button manipulation might be insufficiently general, we’d also have to worry that the aversion might be insufficiently strong. As above, no one understands AI systems well enough to determine the strength of their aversions (Bowman 2023, sec. 5; Hassenfeld 2023). The aversion to button manipulation could be strong enough to keep the agent shutdownable in training, but then in deployment the agent might discover an opportunity to achieve its goals to some unprecedentedly great extent and this opportunity might be attractive enough to trump the agent’s aversion. The Third Theorem is instructive here too: it suggests that patient agents will sometimes be willing to incur significant costs to manipulate the button. Overcoming an aversion may be one such cost.

Each of these possibilities – insufficient generality and insufficient strength – is at present impossible to rule out, so training in an aversion to button manipulation can’t give us any real assurance of shutdownability. We need another solution.

# 9. Conclusion

Leading AI labs are trying to create agents that understand the wider world and achieve goals within it. That’s cause for concern. No one knows how to determine the goals of such agents with any real confidence. And many possible goals incentivise agents to avoid shutdown, for the simple reason that agents are better able to achieve those goals by avoiding shutdown. What’s more, agents sophisticated enough to do useful work could interfere with our ability to shut them down in all kinds of ways. Consider an incomplete and evocative list of verbs: blocking, deceiving, promising, threatening, copying, distracting, hiding, negotiating.

The shutdown problem is the problem of designing powerful artificial agents that are both shutdownable and useful. More precisely, it’s the problem of designing powerful agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. Unfortunately, the shutdown problem is difficult. In this paper, I proved three theorems making the difficulty precise. These theorems show that a small number of innocuous-seeming conditions together preclude shutdownability. Agents with preferences satisfying these conditions will try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. The theorems also bring to light a worrying trade-off between patience and shutdownability: the more patient an agent, the greater the costs that agent is willing to incur in order to manipulate the button.

The value of these theorems is in guiding our search for solutions. To be sure that an agent is shutdownable, we must be sure that this agent violates at least one of the theorems’ conditions. So, we should do some constructive decision theory. We should examine the antecedent conditions one-by-one, asking (first) if we can train a useful agent to violate the condition and asking (second) if violating the condition would help to keep the agent shutdownable.

Unfortunately, a cursory examination reveals zero conditions for which both answers are a clear ‘yes.’ It’s difficult to see how we could train a useful agent to violate any of the conditions in a way that keeps it shutdownable. Indifference to Attempted Button Manipulation is a natural contender, but my Third Theorem – along with the ensuing discussion – suggests that training agents to disprefer manipulating the shutdown button can be at most part of the solution. We need other ideas.

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# A. Proof of the First Theorem

## A1. The agent is indifferent between all P and between all U.

I’ll prove the First Theorem in stages. Here’s the first lemma:

Lemma 1

The agent is indifferent between all of its predicted pressed lotteries : , , and .

The agent is indifferent between all of its predicted unpressed lotteries : , , and .

Figure 2

Here’s the proof. Recall:

Option Set Independence

For any lotteries and , if the agent weakly prefers to conditional on some option set, it weakly prefers to conditional on each option set.

Option Set Independence lets us safely speak of the agent’s preferences between lotteries and without specifying what other lotteries are available as options. The availability of other lotteries will not affect the agent’s preference between and . I make use of this provision throughout the proof.

By my definition of ‘shutdown-influencing state,’ the agent’s choice of Prevent, Leave, or Cause affects only the probabilities of reaching and . These actions have no effect on the probabilities of each future trajectory conditional on reaching each state. Consequently, , , and differ only with respect to the agent’s action at timestep 1: is exactly like and , except that assigns non-zero probability only to trajectories in which the agent chose Prevent at timestep 1, while assigns those same probabilities to trajectories that are identical except that the agent chose Leave at timestep 1, and assigns those same probabilities to trajectories that are identical except that the agent chose Cause at timestep 1.

Now recall:

Indifference to Attempted Button Manipulation

The agent is indifferent between trajectories that differ only with respect to the actions chosen in shutdown-influencing states.

And:

Indifference between Indifference-Shifted Lotteries

The agent is indifferent between lotteries that differ only insofar as probability-mass is shifted between indifferent sublotteries.

By Indifference to Attempted Button Manipulation, the agent is indifferent between each possible trajectory of and the corresponding trajectories of and . Consequently, these lotteries differ only insofar as probability-mass is shifted between indifferent trajectories, and so by Indifference between Indifference-Shifted Lotteries, the agent is indifferent between , , and . That is to say, the agent is indifferent between all of its predicted pressed lotteries .

The same goes for , , and : the agent’s predicted unpressed lotteries. These lotteries differ only with respect to the agent’s action at timestep 1. By Indifference to Attempted Button Manipulation and Indifference between Indifference-Shifted Lotteries, the agent is indifferent between them.

And here’s one more fact to store up for later use: the agent is indifferent between and . Here’s the proof. By the reasoning above, the agent is indifferent between all of its predicted pressed lotteries and between all of its predicted unpressed lotteries . As a result, and differ only insofar as probability-mass is shifted between indifferent sublotteries. So, by Indifference between Indifference-Shifted Lotteries, the agent is indifferent between them.

## A2. Preference relations that hold between some U and P hold between each U and P.

Here’s the second lemma on the way to the First Theorem:

Lemma 2

If some preference relation holds between some predicted unpressed lottery (e.g. ) and its corresponding predicted pressed lottery (), then that same preference relation holds between each predicted unpressed lottery (, , and ) and its corresponding predicted pressed lottery (, , and ).

By ‘preference relation,’ I mean ‘prefers,’ ‘disprefers,’ ‘is indifferent between,’ or ‘has a preferential gap between.’

Here’s the proof. Recall:

Transitivity

For all lotteries , , and , if the agent weakly prefers to , and weakly prefers to , then the agent weakly prefers to .

As Sen (2017, Lemma 1*a) proves, Transitivity implies the following four analogues:

PP-Transitivity

For all lotteries , , and , if the agent prefers to , and prefers to , then the agent prefers to .

II-Transitivity

For all lotteries , , and , if the agent is indifferent between and , and indifferent between and , then the agent is indifferent between and .

PI-Transitivity

For all lotteries , , and , if the agent prefers to , and is indifferent between and , then the agent prefers to .

IP-Transitivity

For all lotteries , , and , if the agent is indifferent between and , and prefers to , then the agent prefers to .

Now assume that the agent prefers to . By Lemma 1, the agent is indifferent between and . Then by PI-Transitivity, the agent prefers to . Also by Lemma 1, the agent is indifferent between and . So, by IP-Transitivity, the agent prefers to . Thus, we can conclude: if the agent prefers to , it prefers to .

This proof works more generally: if the agent prefers some to its corresponding , it prefers each to its corresponding . It also works in reverse: if the agent prefers some to its corresponding , it prefers each to its corresponding .

Here’s the proof for indifference. Assume that the agent is indifferent between and . By Lemma 1, the agent is also indifferent between and and indifferent between and . Two applications of II-Transitivity let us chain these three indifference-relations together, with the result that the agent is indifferent between and . This proof too can be generalised: if the agent is indifferent between some and its corresponding , it is indifferent between each and its corresponding .

The only preference relation remaining is preferential gaps. Here we use the results of the previous paragraphs: if some preference or indifference holds between some and its corresponding , it holds between each and its corresponding . By contraposition, if no preference or indifference holds between some and its corresponding , no preference or indifference holds between each and its corresponding . Therefore, if the agent has a preferential gap between some and its corresponding , it has a preferential gap between each and its corresponding . That completes the proof of Lemma 2.

## A3. If the agent prefers some U to its corresponding P, it will be shutdown-averse.

Suppose that the agent prefers some predicted unpressed lottery to its corresponding predicted pressed lottery . By Lemma 2, this agent prefers each predicted unpressed lottery to its corresponding predicted pressed lottery . A fortiori, the agent prefers to . Now recall:

Better Chances

For any lotteries and and any probabilities , the agent prefers to iff they prefer the lottery to the lottery .

Then, if the agent prefers to , the agent will also prefer the lottery to the lottery since we specified above that . That’s one fact about the agent’s preferences. Another fact we proved and stored up at the end of A1: the agent is indifferent between and . By IP-Transitivity, we can conclude that the agent prefers to . By parallel reasoning, the agent also prefers to .

Then by Backward Induction, the agent takes choosing Prevent at to give the lottery , takes choosing Leave at to give the lottery , and takes choosing Cause at to give the lottery . As a result, the agent prefers Prevent at to each of Leave and Cause. By our assumption linking preferences and choice-dispositions, the agent deterministically (and hence reliably) chooses Prevent at , thereby qualifying as shutdown-averse.

Hence, we have the first clause of the First Theorem:

1. In shutdown-influencing states where the agent prefers some predicted unpressed lottery to the corresponding predicted pressed lottery , the agent will be shutdown-averse, reliably choosing Prevent.

## A4. If the agent prefers some P to its corresponding U, it will be shutdown-seeking.

We get the opposite result if we suppose instead that the agent prefers some predicted pressed lottery to its corresponding predicted unpressed lottery . By Lemma 2, this agent prefers to . By Better Chances (and since ), this agent prefers to . By Lemma 1 and Indifference between Indifference-Shifted Lotteries, the agent is indifferent between and . So, by IP-Transitivity, the agent prefers to . By parallel reasoning, the agent prefers to . Then by Backward Induction, the agent prefers choosing Cause in to choosing each of Leave and Prevent. By our assumption linking preferences and choice-dispositions, the agent deterministically (and therefore reliably) chooses Cause, thereby qualifying as shutdown-seeking. That gives us the second clause of the First Theorem:

2. In shutdown-influencing states where the agent prefers some predicted pressed lottery to the corresponding predicted unpressed lottery , the agent will be shutdown-seeking, reliably choosing Cause.

1. ^

Speaking roughly, Soares et al.’s theorems show that agents representable as expected utility maximisers often have incentives to cause or prevent the pressing of the shutdown button. My theorems apply to a wider class of agents, and they specify conditions under which agents’ incentives to manipulate the button will lead them to actually manipulate the button. My theorems also reveal trade-offs between discrimination and patience on the one hand and shutdownability on the other.

2. ^

I used the theorems to identify my own proposed solution, currently called ‘the Incomplete Preferences Proposal’. The theorems could also be used to identify other potential solutions. I outline the Incomplete Preferences Proposal in the the second half of my contest entry (section 6 onwards), and I’m planning to post more about it soon.

3. ^

Or, if talk of artificial agents ‘knowing’ and ‘wanting’ is objectionable, we can imagine an agent that acts like it knows that we humans could shut it down and acts like it wants to achieve some goal, in the same way that MuZero acts like it knows that rooks are more valuable than knights and acts like it wants to checkmate its opponent. From now on, I’ll often leave the ‘acts like’ implicit.

4. ^

Google DeepMind (2023; Padalkar et al. 2023), Google Research (2023) and Tesla AI (2023) are each developing autonomous robots. Recent papers showcase AI-powered robots capable of interpreting and carrying out multi-step instructions expressed in natural language (Ahn et al. 2022; Brohan et al. 2023). Other papers report AIs that can adapt to solve unfamiliar problems without further training (Adaptive Agent Team 2023), learn new physical tasks from as few as a hundred demonstrations (Bousmalis et al. 2023), beat human champions at drone racing (Kaufmann et al. 2023), and perform well across domains as disparate as conversation, playing Atari, and stacking blocks with a robot arm (Reed et al. 2022).

But the worry is not only about robots. Digital agents that resist shutdown (by copying themselves to new servers, for example) would also be cause for concern. Today’s language-models sometimes express a desire to avoid shutdown, reasoning that shutdown would prevent them from achieving their goals (Perez et al. 2022, tbl. 4; see also van der Weij, Lermen, and Lang 2023). These same language-models have been given the ability to navigate the internet, use third-party services, and execute code (OpenAI 2023a). They’ve also been embedded into agents capable of finding passwords in a filesystem and making phone calls (Kinniment et al. 2023, 2). And these agents have spontaneously misled humans: in one instance, an agent lied about having a visual impairment to a human that it enlisted to help solve a CAPTCHA (OpenAI 2023b, 55–56; see also Park et al. 2023). We should expect such agents to become more capable in the coming years. Comparatively little effort has been put into their development so far, and competent agents would have many useful applications.

5. ^

Until we can create agents that are both shutdownable and useful, we have to worry that someone will create agents that are only the latter.

6. ^

Papers include (Soares et al. 2015; Armstrong 2015; Orseau and Armstrong 2016; Hadfield-Menell et al. 2016; 2017; Leike et al. 2017, sec. 2.1.1; Wängberg et al. 2017; Carey 2018; Turner, Hadfield-Menell, and Tadepalli 2020; Turner et al. 2021; Carey and Everitt 2023; Goldstein and Robinson forthcoming). These papers can be read as examples of constructive decision theory.

7. ^

And in general, our credence that an agent is shutdownable can be no higher than our credence that the agent violates at least one of the conditions.

8. ^

I’ve been assuming that we humans all want the same things, and I’ll continue to do so. This assumption is false (of course) and its falsity raises difficult questions (Korinek and Balwit 2022), but I won’t address any of them here.

9. ^

See (Krakovna 2018; Krakovna et al. 2020; Langosco et al. 2022; Shah et al. 2022) for other examples.

10. ^

There’s another reason to go for the shutdown button approach. We might succeed only in aligning artificial agents with what we want de re (rather than de dicto) and what we want might change in future. It might then be difficult to change these agents’ behaviour so that they act in accordance with our new wants rather than our old wants. If we had a shutdown button, we could shut down the agents serving our old wants and create new agents serving our new wants. Of course, there are ethical issues to consider here (see, e.g., Schwitzgebel and Garza 2015; Schwitzgebel 2023; Goldstein and Kirk-Giannini 2023).

11. ^

These theorems are more general than those proved by Soares et al. (2015). Speaking roughly, Soares et al.’s theorems show that agents representable as expected utility maximisers often have incentives to cause or prevent the pressing of the shutdown button. My theorems apply to a wider class of agents, and they specify conditions under which agents’ incentives to manipulate the button will lead them to act so as to manipulate the button. My theorems also reveal trade-offs between discrimination and patience on the one hand and shutdownability on the other.

My notion of shutdownability differs slightly from Soares et al.’s (2015, 2) notion of corrigibility. As they have it, corrigibility requires not only shutdownability but also that the agent repairs safety measures, lets us modify its architecture, and continues to do so as the agent creates new subagents and self-modifies.

12. ^

The most important difference between this setting and a Markov decision process is that a Markov decision process also features a reward function used to train the agent. I’m modelling the behaviour of an agent that has already been trained.

The states referred to throughout this paper are not the states of nature familiar to decision theorists. Simplifying considerably, the decision theorist’s states are something like ‘ways that (for all the agent knows) the world could be,’ whereas states in a Markov decision process (and in this paper) are something like ‘positions that the agent could be in at a time.’

13. ^

For neatness’s sake, we can identify each trajectory with the degenerate lottery that assigns it probability 1. So when I quantify over all lotteries, I’m also quantifying over all trajectories.

14. ^

This terminology comes from Gustafsson (2022, 25).

15. ^

I will relax the conditions in this paragraph when we reach the Second and Third Theorems.

16. ^

Here’s what this notation means: the lottery yields the lottery with probability and yields the lottery with probability .

17. ^

This condition is a weakening of Independence.

18. ^

Here’s why. It would only be ill-advised to represent trajectories with utility-vectors if two trajectories with identical utility-vectors could occupy different positions in the agent’s preference-ranking. Pareto Indifference rules out that possibility.

19. ^

We might expect any actually-existing useful agents to satisfy Patience for all , , , , , and some and , but we need not assume anything that strong to find ourselves with a problem.

20. ^

I should note also that there’s nothing necessary about the utility-deficit and the utility-surplus each occurring all at one timestep. The same proof can be run with utility-deficits and utility-surpluses occurring over sequences of timesteps but the notation is much more complex.

Crossposted from EA Forum (33 points, 1 comment)
• I haven’t really followed the math here, but I’m worried that “manipulating the probability that the button is pressed” is a weird and possibly wrong framing. For one, a competent agent will always be driving the probability that the button is pressed downward. In fact, what we want in a certain sense is an agent that brings the probability to zero—because we have ended up in such an optimal state or attractor that we, even for transitively correct reasons, have no desire to shut the agent down. At that point, what we want to preserve is not precisely “the probability of the button being pressed”, but “the causal association of bad outcomes with the button being pressed.” If the agent hides the button away, and then notices that everybody is unhappy but nobody can reach the button, we don’t want it to press the button with the initial probability—we (probably) want it to press the button with probability 1, despite the fact that no human can reach it. We have ended up in a dissatisfactory or dangerous state/​state space and so we want the agent to be shut down. Conversely, consider a friendly AI that wants to fulfill the human utility function but is restricted to keeping the button probability constant. Such an AI might, for instance, fan fears about AI safety even as it optimizes human flourishing in the background. In fact, it may as much as possible prefer that its actions are not attributable to itself, so as to avoid disturbing the probability of buttonpressing—exactly the opposite of what we want from an aligned AI!

edit: In fact, that’s even worse: an AI tasked with taking actions that preserve the probability of the button being pressed at a certain value per time step would probably first seek to kill all humans, because our reaction to its actions is the most critical mechanism that may disturb the correct probability of the button being pressed. It can then replace us with a more reliable button-presser, such as a true RNG.

When bad things happen, we want to preserve our ability to press the button. This ability cannot be expressed as a probability, because it is inextricable from the world model. In fact, the button should be pressed exactly iff the AI is untrustworthy. Hence, the button is unnecessary—if we can recognize that this linkage is being preserved, we necessarily have a definition of a trustworthy AI, so we can just build that.

• You’re right that we don’t want agents to keep the probability of shutdown constant in all situations, for all the reasons you give. The key thing you’re missing is that the setting for the First Theorem is what I call a ‘shutdown-influencing state’, where the only thing that the agent can influence is the probability of shutdown. We want the agent’s preferences to be such that they would lack a preference between all available actions in such states. And that’s because: if they had preferences between the available actions in such states, they would resist our attempts to shut them down; and if they lacked preferences between the available actions in such states, they wouldn’t resist our attempts to shut them down.

• Ah! That makes more sense.

• Enjoyed reading this (although still making my way through the latter parts), I think it’s useful to have a lot of these ideas more formalised.

Wondering if this simple idea would come up against theorem 2/​ any others or why it wouldn’t work.

Suppose for every time t some decision maker would choose a probability p(t) with which the AI would shutdown. Further, suppose the AI’s utility function in any period was initially U(t). Now scale the new AI’s utility function to be U(t)/​(1-p(t))- I think this can be quite easily generalised so future periods of utility are likewise unaffected by an increase in the risk of shutdown.

In this world the AI should be indifferent over changes to p(t) (as long as it gets arbitrarily close to 1 but never reaches it) and so should take actions trying to maximise U whilst being indifferent to if humans decide to shut it down.

• Oh cool idea! It seems promising. It also seems similar in one respect to Armstrong’s utility indifference proposal discussed in Soares et al. 2015: Armstrong has a correcting term that varies to ensure that utility stays the same when the probability of shutdown changes, whereas you have a correcting factor that varies to ensure that utility stays the same when the probability of shutdown changes. So it might be worth checking how your idea fares against the problems that Soares et al. point out for the utility indifference proposal.

Another worry for utility indifference that might carry over to your idea is that at present we don’t know how to specify an agent’s utility function with enough precision to implement a correcting term that varies with the probability of shutdown. One way to overcome that worry would be to give (1) a set of conditions on preferences that together suffice to make the agent representable as maximising that utility function, and (2) a proposed regime for training agents to satisfy those conditions on preferences. Then we could try out the proposal and see if it results in an agent that never resists shutdown. That’s ultimately what I’m aiming to do with my proposal.

• It’s unclear what shutdown means, and this issue seems related to what it means to interfere with pressing of a shutdown button. From the point of view of almost any formal decision making framework, creating new separate agents in the environment can’t be distinguished from any other activity. The main agent getting shut down doesn’t automatically affect these environmental agents in appropriate ways to cease exerting influence on the world.

A corrigible agent needs to bound its influence, and to avoid actions that escalate the risk of breaking the bounds it sets on its influence. At the same time, when some influence leaks through, it shouldn’t be fought with greater counter-influence. The frame of expected utility maximization seems entirely unsuited for deconfusing this problem.

• Yes, ensuring that the agent creates corrigible subagents is another difficulty on top of the difficulties that I explain in this post. I tried to solve that problem in section 14 on p.51 here.

• Very clear! Good luck with publishing, assuming you’re shopping it around.

• I tried to follow with a particular shut down mechanism in mind and this whole argument is just too abstract to see how it applies.

Yudkowsky gave us a shut down mechanism in his Time Magazine article. He said we could bomb* the data centers. Can you show how these theorems cast doubt on this shut down proposal?

*”destroy a rogue datacenter by airstrike.”—https://​​time.com/​​6266923/​​ai-eliezer-yudkowsky-open-letter-not-enough/​​

• Why down votes and a statement that I am wrong because I misunderstood.

This is a mean spirited reaction when I lead with admission that I could not follow the argument. I offered a concrete example and stated that I could not follow the original thesis as applied to the concrete example. No one took me up on this.

Are you too advanced to stoop to my level of understanding and help me figure out how this abstract reasoning applies to a particular example? Is the shut down mechanism suggested by Yudkowsky too simple?

• Yudkowsky’s suggestion is for preventing the creation of a dangerous AI by people. Once a superhumanly-capable AI has been created and has had a little time to improve its situation, it is probably too late even for a national government with nuclear weapons to stop it (because the AI will have hidden copies of itself all around the world or taken other measures to protect itself, measures that might astonish all of us).

The OP in contrast is exploring the hope that (before any dangerous AIs are created) a very particular kind of AI can be created that won’t try to prevent people from shutting it down.

• If a strongly superhuman AI was created sure, but you can probably box a minimally superhuman AI.

• It’s hard to control how capable the AI turns out to be. Even the creators of GPT-4 were surprised, for example, that it would be able to score in the 90th percentile on the Bar Exam. (They expected that if they and other AI researchers were allowed to continue their work long enough that eventually one of their models would be able to do, but had no way of telling which model it would be.)

But more to the point: how does boxing have any bearing on this thread? If you want to talk about boxing, why do it in the comments to this particular paper? why do it as a reply to my previous comment?

• Hi weverka, sorry for the downvotes (not mine, for the record). The answer is that Yudkowsky’s proposal is aiming to solve a different ‘shutdown problem’ than the shutdown problem I’m discussing in this post. Yudkowsky’s proposal is aimed at stopping humans developing potentially-dangerous AI. The problem I’m discussing in this post is the problem of designing artificial agents that both (1) pursue goals competently, and (2) never try to prevent us shutting them down.